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Gait stability is an important indicator of human health and physical ability, and it is of great significance for early detection, diagnosis, and rehabilitation of diseases. This article proposes the first comprehensive and in-depth method for quantitatively evaluating gait stability using electrostatic gait signals (EGSs). A quantitative evaluation of gait stability was conducted on the EGSs of ten healthy subjects (HSs) and ten hemiplegic patients (HPs) from three perspectives: variability of gait phase temporal parameters, nonlinear local dynamic stability, and energy frequency band distribution differences. The coefficient of variation CV (x) reflects the degree of variation of each temporal parameter. Local dynamic stability is characterized by using the short-term largest Lyapunov exponent (ₒ^) to reflect the sensitivity to local perturbations and to characterize gait stability. Power spectral entropy (PSE) is used to quantify the complexity and instability of the signal in the frequency domain to characterize gait stability. The results showed that the CV (x), ₒ^, and PSE were all significantly greater in HPs than in HSs. The Mann–Whitney test was used to perform significance testing on each indicator between the HS and HP groups. The results showed that, except for the variability of the affected side support phase CV (T ₋₇₈), the variability of the other 13 gait temporal parameters, short-term largest Lyapunov exponent (ₒ^), and PSE differed significantly between the groups (p). This research explores a feasible technical approach to using EGSs for gait analysis and quantitatively assessing the gait stability of subjects.
Qin et al. (Thu,) studied this question.